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Research Implications for Computer Science Education based on Darmstadt Model

Year 2021, , 39 - 61, 27.04.2021

Yasemin Gülbahar Güven , Filiz Kalelioğlu

https://doi.org/10.18009/jcer.806875

Abstract

The purpose of the current study is to examine published studies in computer science education in a systematic way, and to present a history of the research and new research trends in this area. This research study reports the findings of the systematic literature review according to the educational relevant areas dimension of the Darmstadt Model. The procedures of systematic text analysis were performed as a qualitative content analysis. Prior to the systematic text analysis, the primary term ‘computer science education and K-12’ was searched for along with data in the abstract, title and keyword section for publications between 2013 and 2018 in the databases and digital libraries of Academic Search Complete, Business Source Complete, Eric, Science Direct, and the IEEE Digital Library. A total of 87 articles formed the sample of the study. Although the current study was limited to the stated journal articles, it provides insight to the field by shedding light on important issues relevant to future research studies.

Keywords

computer science education research systematic literature review

References

  • Aleksić, V., & Ivanović, M. (2016). Introductory programming subject in European higher education. Informatics in Education, 15(2), 163-182.
  • Benotti, L., Martinez, M. C., & Schapachnik, F. (2018). A tool for introducing computer science with automatic formative assessment. IEEE Transactions on Learning Technologies, 11(2), 179-192.
  • Blikstein, P. (2018). Pre-College computer science education: a survey of the field. Mountain View, CA: Google LLC. Retrieved from https://goo.gl/gmS1Vm.
  • Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education-Implications for policy and practice (No. JRC104188). Luxembourg: Publications Office of the European Union. Retrieved from http://publications.jrc.ec.europa.eu/repository/bitstream/JRC104188/jrc104188_computhinkreport.pdf.
  • Code Advocacy Coalition. (2018). 2018 State of computer science education, policy and implementation. Retrieved from https://code.org/files/2018_state_of_cs.pdf.
  • Coleman, L. O., Gibson, P., Cotten, S. R., Howell-Moroney, M., & Stringer, K. (2016). Integrating computing across the curriculum: The impact of internal barriers and training intensity on computer integration in the elementary school classroom. Journal of Educational Computing Research, 54(2), 275-294.
  • Gretter, S., Yadav, A., Sands, P., & Hambrusch, S. (2019). Equitable learning environments in k-12 computing: teachers’ views on barriers to diversity. ACM Transactions in Computing Education, 19(3), 1-16.
  • Hubwieser, P. (2013). The Darmstadt model: A first step towards a research framework for computer science education in schools. In Informatics in Schools. Sustainable Informatics Education for Pupils of all Ages. In I. Diethelm & R. T. Mittermeir (Eds.), Proceeding of the 6th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (ISSEP’13) (pp. 1-14). Berlin, Germany: Springer.
  • Hubwieser, P., Armoni, M., Brinda, T., Dagiene, V., Diethelm, I., Giannakos, M. N.,…Schubert, S. E. (2011). Computer science/informatics in secondary education. In L. Adams & J. J. Jurgens (Eds.), Proceedings of the 16th Annual Conference Reports on Innovation and Technology in Computer Science Education—Working Group Reports (pp. 19-38). New York, NY: ACM. doi:10.1145/2078856.2078859
  • Hubwieser, P., Armoni, M., & Giannakos, M. N. (2015). How to implement rigorous computer science education in K-12 schools? Some answers and many questions. ACM Transactions in Computing Education, 15(2), 1-12. DOI: http://dx.doi.org/10.1145/2729983.
  • Kalelioglu, F., & Gulbahar, Y. (2014). The Effects of teaching programming via scratch on problem solving skills: a discussion from learners’ perspective. Informatics in Education, 13(1), 33-50.
  • Kim, S. (2018). ICT for children of immigrants: Indirect and total effects via self-efficacy on math performance. Journal of Educational Computing Research, 55(8), 1168-1200.
  • Kong, S.-C., Li, R. K.-Y., & Kwok, R. C.-W. (2018). Measuring parents’ perceptions of programming education in p-12 schools: scale development and validation. Journal of Educational Computing Research, 57(5), 1260-1280.
  • Kožuh, I., Krajnc, R., Hadjileontiadis, L. J., & Debevc, M. (2018). Assessment of problem solving ability in novice programmers. PloS one, 13(9), 1-21.
  • Kucuk, S., & Sisman, B. (2018). Pre-Service teachers’ experiences in learning robotics design and programming. Informatics in Education, 17(2), 301-320.
  • Kynigos, C., & Grizioti, M. (2018). Programming approaches to computational thinking: integrating turtle geometry, dynamic manipulation and 3d space. Informatics in Education, 17(2), 321-340.
  • Lee, A. (2015). Determining the effects of computer science education at the secondary level on STEM major choices in postsecondary institutions in the United States. Computers & Education, 88, 241-255.
  • Lin, G.-Y. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53(4), 540-561.
  • Liu, Z., Zhi, R., Hicks, A., & Barnes, T. (2017). Understanding problem solving behavior of 6–8 graders in a debugging game. Computer Science Education, 27(1), 1-29.
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61.
  • Menekse, M. (2015). Computer science teacher professional development in the United States: a review of studies published between 2004 and 2014. Computer Science Education, 25(4), 325-350.
  • Özbey,S. & Köyceğiz-Gözeler,M. (2020). A study on the effect of the social skill education on the academic self respect and problem solving skills of the pre-school children. International e-Journal of Educational Studies (IEJES), 4 (8), 176-189. DOI: 10.31458/iejes.727590.
  • Price, C. B., & Price-Mohr, R. M. (2018). An evaluation of primary school children coding using a text-based language (Java). Computers in the Schools, 35(4), 284-301.
  • Shaw, R. S. (2017). The learning performance of different knowledge map construction methods and learning styles moderation for programming language learning. Journal of Educational Computing Research, 56(8), 1407-1429.
  • Stephenson, C., Gal-Ezer, J., Haberman, B., & Verno, A. (2005). The new educational imperative: Improving high school computer science education. Using worldwide research and professional experience to improve U.S. Schools. Final Report of the CSTA Curriculum Improvement Task Force. New York, NY: ACM CSTA. Retrieved from https://cse.sc.edu/~buell/References/StudentRecruiting/CSTA-WhitePaperNC.pdf.
  • The Royal Society. (2012). Shutdown or Restart. The Way Forward for Computing in UK Schools. Retrieved from https://royalsociety.org/-/media/education/computing-in-schools/2012-01-12-computing-in-schools.pdf
  • Veerasamy, A. K., D’Souza, D., Lindén, R., & Laakso, M. J. (2018). The impact of prior programming knowledge on lecture attendance and final exam. Journal of Educational Computing Research, 56(2), 226-253.
  • Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running on empty: The Failure to Teach K–12 Computer Science in the Digital Age. Retrieved from https://runningonempty.acm.org/fullreport2.pdf.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
  • World Economic Forum. (2016). The future of jobs employment, skills and workforce strategy for the fourth industrial revolution. Global Challenge Insight Report. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf.
  • World Economic Forum. (2020). The future of jobs report. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf.
  • Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016). Expanding computer science education in schools: understanding teacher experiences and challenges. Computer Science Education, 26(4), 235-254.
  • Zendler, A., Klaudt, D., & Seitz, C. (2014). Empirical determination of competence areas to computer science education. Journal of Educational Computing Research, 51(1), 71-89.
  • Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590.

Research Implications for Computer Science Education based on Darmstadt Model

Year 2021, , 39 - 61, 27.04.2021

Yasemin Gülbahar Güven , Filiz Kalelioğlu

https://doi.org/10.18009/jcer.806875

Abstract

The purpose of the current study is to examine published studies in computer science education in a systematic way, and to present a history of the research and new research trends in this area. This research study reports the findings of the systematic literature review according to the educational relevant areas dimension of the Darmstadt Model. The procedures of systematic text analysis were performed as a qualitative content analysis. Prior to the systematic text analysis, the primary term ‘computer science education and K-12’ was searched for along with data in the abstract, title and keyword section for publications between 2013 and 2018 in the databases and digital libraries of Academic Search Complete, Business Source Complete, Eric, Science Direct, and the IEEE Digital Library. A total of 87 articles formed the sample of the study. Although the current study was limited to the stated journal articles, it provides insight to the field by shedding light on important issues relevant to future research studies.

Keywords

Computer science education research systematic literature review

References

  • Aleksić, V., & Ivanović, M. (2016). Introductory programming subject in European higher education. Informatics in Education, 15(2), 163-182.
  • Benotti, L., Martinez, M. C., & Schapachnik, F. (2018). A tool for introducing computer science with automatic formative assessment. IEEE Transactions on Learning Technologies, 11(2), 179-192.
  • Blikstein, P. (2018). Pre-College computer science education: a survey of the field. Mountain View, CA: Google LLC. Retrieved from https://goo.gl/gmS1Vm.
  • Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education-Implications for policy and practice (No. JRC104188). Luxembourg: Publications Office of the European Union. Retrieved from http://publications.jrc.ec.europa.eu/repository/bitstream/JRC104188/jrc104188_computhinkreport.pdf.
  • Code Advocacy Coalition. (2018). 2018 State of computer science education, policy and implementation. Retrieved from https://code.org/files/2018_state_of_cs.pdf.
  • Coleman, L. O., Gibson, P., Cotten, S. R., Howell-Moroney, M., & Stringer, K. (2016). Integrating computing across the curriculum: The impact of internal barriers and training intensity on computer integration in the elementary school classroom. Journal of Educational Computing Research, 54(2), 275-294.
  • Gretter, S., Yadav, A., Sands, P., & Hambrusch, S. (2019). Equitable learning environments in k-12 computing: teachers’ views on barriers to diversity. ACM Transactions in Computing Education, 19(3), 1-16.
  • Hubwieser, P. (2013). The Darmstadt model: A first step towards a research framework for computer science education in schools. In Informatics in Schools. Sustainable Informatics Education for Pupils of all Ages. In I. Diethelm & R. T. Mittermeir (Eds.), Proceeding of the 6th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (ISSEP’13) (pp. 1-14). Berlin, Germany: Springer.
  • Hubwieser, P., Armoni, M., Brinda, T., Dagiene, V., Diethelm, I., Giannakos, M. N.,…Schubert, S. E. (2011). Computer science/informatics in secondary education. In L. Adams & J. J. Jurgens (Eds.), Proceedings of the 16th Annual Conference Reports on Innovation and Technology in Computer Science Education—Working Group Reports (pp. 19-38). New York, NY: ACM. doi:10.1145/2078856.2078859
  • Hubwieser, P., Armoni, M., & Giannakos, M. N. (2015). How to implement rigorous computer science education in K-12 schools? Some answers and many questions. ACM Transactions in Computing Education, 15(2), 1-12. DOI: http://dx.doi.org/10.1145/2729983.
  • Kalelioglu, F., & Gulbahar, Y. (2014). The Effects of teaching programming via scratch on problem solving skills: a discussion from learners’ perspective. Informatics in Education, 13(1), 33-50.
  • Kim, S. (2018). ICT for children of immigrants: Indirect and total effects via self-efficacy on math performance. Journal of Educational Computing Research, 55(8), 1168-1200.
  • Kong, S.-C., Li, R. K.-Y., & Kwok, R. C.-W. (2018). Measuring parents’ perceptions of programming education in p-12 schools: scale development and validation. Journal of Educational Computing Research, 57(5), 1260-1280.
  • Kožuh, I., Krajnc, R., Hadjileontiadis, L. J., & Debevc, M. (2018). Assessment of problem solving ability in novice programmers. PloS one, 13(9), 1-21.
  • Kucuk, S., & Sisman, B. (2018). Pre-Service teachers’ experiences in learning robotics design and programming. Informatics in Education, 17(2), 301-320.
  • Kynigos, C., & Grizioti, M. (2018). Programming approaches to computational thinking: integrating turtle geometry, dynamic manipulation and 3d space. Informatics in Education, 17(2), 321-340.
  • Lee, A. (2015). Determining the effects of computer science education at the secondary level on STEM major choices in postsecondary institutions in the United States. Computers & Education, 88, 241-255.
  • Lin, G.-Y. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53(4), 540-561.
  • Liu, Z., Zhi, R., Hicks, A., & Barnes, T. (2017). Understanding problem solving behavior of 6–8 graders in a debugging game. Computer Science Education, 27(1), 1-29.
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61.
  • Menekse, M. (2015). Computer science teacher professional development in the United States: a review of studies published between 2004 and 2014. Computer Science Education, 25(4), 325-350.
  • Özbey,S. & Köyceğiz-Gözeler,M. (2020). A study on the effect of the social skill education on the academic self respect and problem solving skills of the pre-school children. International e-Journal of Educational Studies (IEJES), 4 (8), 176-189. DOI: 10.31458/iejes.727590.
  • Price, C. B., & Price-Mohr, R. M. (2018). An evaluation of primary school children coding using a text-based language (Java). Computers in the Schools, 35(4), 284-301.
  • Shaw, R. S. (2017). The learning performance of different knowledge map construction methods and learning styles moderation for programming language learning. Journal of Educational Computing Research, 56(8), 1407-1429.
  • Stephenson, C., Gal-Ezer, J., Haberman, B., & Verno, A. (2005). The new educational imperative: Improving high school computer science education. Using worldwide research and professional experience to improve U.S. Schools. Final Report of the CSTA Curriculum Improvement Task Force. New York, NY: ACM CSTA. Retrieved from https://cse.sc.edu/~buell/References/StudentRecruiting/CSTA-WhitePaperNC.pdf.
  • The Royal Society. (2012). Shutdown or Restart. The Way Forward for Computing in UK Schools. Retrieved from https://royalsociety.org/-/media/education/computing-in-schools/2012-01-12-computing-in-schools.pdf
  • Veerasamy, A. K., D’Souza, D., Lindén, R., & Laakso, M. J. (2018). The impact of prior programming knowledge on lecture attendance and final exam. Journal of Educational Computing Research, 56(2), 226-253.
  • Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running on empty: The Failure to Teach K–12 Computer Science in the Digital Age. Retrieved from https://runningonempty.acm.org/fullreport2.pdf.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
  • World Economic Forum. (2016). The future of jobs employment, skills and workforce strategy for the fourth industrial revolution. Global Challenge Insight Report. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf.
  • World Economic Forum. (2020). The future of jobs report. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf.
  • Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016). Expanding computer science education in schools: understanding teacher experiences and challenges. Computer Science Education, 26(4), 235-254.
  • Zendler, A., Klaudt, D., & Seitz, C. (2014). Empirical determination of competence areas to computer science education. Journal of Educational Computing Research, 51(1), 71-89.
  • Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590.
There are 34 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Review Article
Authors

Yasemin Gülbahar Güven 0000-0002-1726-3224

Filiz Kalelioğlu 0000-0002-7729-5674

Publication Date April 27, 2021
Submission Date October 19, 2020
Acceptance Date December 27, 2020
Published in Issue Year 2021

Cite

APA Gülbahar Güven, Y., & Kalelioğlu, F. (2021). Research Implications for Computer Science Education based on Darmstadt Model. Journal of Computer and Education Research, 9(17), 39-61. https://doi.org/10.18009/jcer.806875

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Önemli: "Yazar adından yapılan yayın/atıf taramalarında isim benzerlikleri, soyadı değişikliği, Türkçe harf içeren isimler, farklı yazımlar, kurum değişiklikleri gibi durumlar sorun oluşturabilmektedir. Bu nedenle araştırmacıların tanımlayıcı kimlik/numara (ID) edinmeleri önem taşımaktadır. ULAKBİM TR Dizin sistemlerinde tanımlayıcı ID bilgilerine yer verilecektir.

Standardizasyonun sağlanabilmesi ve YÖK ile birlikte yürütülecek ortak çalışmalarda ORCID kullanılacağı için, TR Dizin’de yer alan veya yer almak üzere başvuran dergilerin, yazarlardan ORCID bilgilerini talep etmeleri ve dergide/makalelerde bu bilgiye yer vermeleri tavsiye edilmektedir. ORCID, Open Researcher ve Contributor ID'nin kısaltmasıdır.  ORCID, Uluslararası Standart Ad Tanımlayıcı (ISNI) olarak da bilinen ISO Standardı (ISO 27729) ile uyumlu 16 haneli bir numaralı bir URI'dir. http://orcid.org adresinden bireysel ORCID için ücretsiz kayıt oluşturabilirsiniz. "